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Register a new userA census of cool core galaxy clusters in IllustrisTNG
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David J. Barnes
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The thermodynamic structure of hot gas in galaxy clusters is sensitive to astrophysical processes and typically difficult to model with galaxy formation simulations. We explore the fraction of cool-core (CC) clusters in a large sample of $370$ clusters from IllustrisTNG, examining six common CC definitions. IllustrisTNG produces continuous CC criteria distributions, the extremes of which are classified as CC and non-cool-core (NCC), and the criteria are increasingly correlated for more massive clusters. At $z=0$, the CC fractions for $2$ criteria are in reasonable agreement with the observed fractions but the other $4$ CC fractions are lower than observed. This result is partly driven by systematic differences between the simulated and observed gas fraction profiles. The simulated CC fractions with redshift show tentative agreement with the observed fractions, but linear fits demonstrate that the simulated evolution is steeper than observed. The conversion of CCs to NCCs appears to begin later and act more rapidly in the simulations. Examining the fraction of CCs and NCCs defined as relaxed we find no evidence that CCs are more relaxed, suggesting that mergers are not solely responsible for disrupting CCs. A comparison of the median thermodynamic profiles defined by different CC criteria shows that the extent to which they evolve in the cluster core is dependent on the CC criteria. We conclude that the thermodynamic structure of galaxy clusters in IllustrisTNG shares many similarities with observations, but achieving better agreement most likely requires modifications of the underlying galaxy formation model.
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The origin of the diverse population of galaxy clusters remains an unexplained aspect of large-scale structure formation and cluster evolution. We present a novel method of using X-ray images to identify cool core (CC), weak cool core (WCC), and non cool core (NCC) clusters of galaxies, that are defined by their central cooling times. We employ a convolutional neural network, ResNet-18, which is commonly used for image analysis, to classify clusters. We produce mock Chandra X-ray observations for a sample of 318 massive clusters drawn from the IllustrisTNG simulations. The network is trained and tested with low resolution mock Chandra images covering a central 1 Mpc square for the clusters in our sample. Without any spectral information, the deep learning algorithm is able to identify CC, WCC, and NCC clusters, achieving balanced accuracies (BAcc) of 92%, 81%, and 83%, respectively. The performance is superior to classification by conventional methods using central gas densities, with an average BAcc = 81%, or surface brightness concentrations, giving BAcc = 73%. We use Class Activation Mapping to localize discriminative regions for the classification decision. From this analysis, we observe that the network has utilized regions from cluster centers out to r~300 kpc and r~500 kpc to identify CC and NCC clusters, respectively. It may have recognized features in the intracluster medium that are associated with AGN feedback and disruptive major mergers.
In this work we propose a new diagnostic to segregate cool core (CC) clusters from non-cool core (NCC) clusters by studying the two-dimensional power spectra of the X-ray images observed with the Chandra X-ray observatory. Our sample contains 41 members ($z=0.01sim 0.54$), which are selected from the Chandra archive when a high photon count, an adequate angular resolution, a relatively complete detector coverage, and coincident CC-NCC classifications derived with three traditional diagnostics are simultaneously guaranteed. We find that in the log-log space the derived image power spectra can be well represented by a constant model component at large wavenumbers, while at small wavenumbers a power excess beyond the constant component appears in all clusters, with a clear tendency that the excess is stronger in CC clusters. By introducing a new CC diagnostic parameter, i.e., the power excess index (PEI), we classify the clusters in our sample and compare the results with those obtained with three traditional CC diagnostics. We find that the results agree with each other very well. By calculating the PEI values of the simulated clusters, we find that the new diagnostic works well at redshifts up to 0.5 for intermediately sized and massive clusters with a typical Chandra or XMM pointing observation. The new CC diagnostic has several advantages over its counterparts, e.g., it is free of the effects of the commonly seen centroid shift of the X-ray halo caused by merger event, and the corresponding calculation is straightforward, almost irrelevant to the complicated spectral analysis.
We present a systematic study of gas density perturbations in cool cores of high-mass galaxy clusters. We select 12 relaxed clusters from the Cluster Lensing And Supernova survey with Hubble (CLASH) sample and analyze their cool core features observed with the Chandra X-ray Observatory. We focus on the X-ray residual image characteristics after subtracting their global profile of the X-ray surface brightness distribution. We find that all the galaxy clusters in our sample have, at least, both one positive and one negative excess regions in the X-ray residual image, indicating the presence of gas density perturbations. We identify and characterize the locally perturbed regions using our detection algorithm, and extract X-ray spectra of the intracluster medium (ICM). The ICM temperature in the positive excess region is lower than that in the negative excess region, whereas the ICM in both regions is in pressure equilibrium in a systematic manner. These results indicate that gas sloshing in cool cores takes place in more than 80% of relaxed clusters (95% CL). We confirm this physical picture by analyzing synthetic X-ray observations of a cool-core cluster from a hydrodynamic simulation, finding that our detection algorithm can accurately extract both the positive and negative excess regions and can reproduce the temperature difference between them. Our findings support the picture that the gas density perturbations are induced by gas sloshing, and a large fraction of cool-core clusters have undergone gas sloshing, indicating that gas sloshing may be capable of suppressing runaway cooling of the ICM.
We investigate the thermodynamic and chemical structure of the intracluster medium (ICM) across a statistical sample of 20 galaxy clusters analysed with the Chandra X-ray satellite. In particular, we focus on the scaling properties of the gas density, metallicity and entropy and the comparison between clusters with and without cool cores (CCs). We find marked differences between the two categories except for the gas metallicity, which declines strongly with radius for all clusters (Z ~ r^{-0.31}), outside ~0.02 r500. The scaling of gas entropy is non-self-similar and we find clear evidence of bimodality in the distribution of logarithmic slopes of the entropy profiles. With only one exception, the steeper sloped entropy profiles are found in CC clusters whereas the flatter slope population are all non-CC clusters. We explore the role of thermal conduction in stabilizing the ICM and conclude that this mechanism alone is sufficient to balance cooling in non-CC clusters. However, CC clusters appear to form a distinct population in which heating from feedback is required in addition to conduction. Under the assumption that non-CC clusters are thermally stabilized by conduction alone, we find the distribution of Spitzer conduction suppression factors, f_c, to be log-normal, with a log (base 10) mean of -1.50+/-0.03 (i.e. f_c=0.032) and log standard deviation 0.39+/-0.02.
Radio halos are diffuse synchrotron sources on scales of ~1 Mpc that are found in merging clusters of galaxies, and are believed to be powered by electrons re-accelerated by the merger-driven turbulence. We present measurements of extended radio emission on similarly large scales in two clusters of galaxies hosting cool cores: Abell 2390 and Abell 2261. The analysis is based on interferometric imaging with the JVLA, VLA and GMRT. We present detailed radio images of the targets, subtract the compact emission components, and measure the spectral indices for the diffuse components. The radio emission in A2390 extends beyond a known sloshing-like brightness discontinuity, and has a very steep in-band spectral slope at 1.5 GHz that is similar to some known ultra-steep spectrum radio halos. The diffuse signal in A2261 is more extended than in A2390 but has lower luminosity. X-ray morphological indicators, derived from XMM-Newton X-ray data, place these clusters in the category of relaxed or regular systems, although some asymmetric features that can indicate past minor mergers are seen in the X-ray brightness images. If these two Mpc-scale radio sources are categorized as giant radio halos, they question the common assumption of radio halos occurring exclusively in clusters undergoing violent merging activity, in addition to commonly used criteria in distinguishing between radio halos and mini-halos.
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